1. Outlier Detection & Analysis
By:
Eric Poulin
Colin Yu

2. Outlier - Outline Introduction / Motivation / Definition
Statistical-based Detection
Distribution-based, depth-based
Deviation-based Method
Sequential exception, OLAP data cube
Distance-based Detection
Index-based, nested-loop, cell-based, local-outliers
Questions

3. Introduction Traditional Data Mining Categories Majority of Objects
Dependency detection
Class identification
Class description
Exceptions
Exception/outlier detection

4. Motivation for Outlier Analysis
Fraud Detection (Credit card, telecommunications, criminal activity in e-Commerce)
Customized Marketing (high/low income buying habits)
Medical Treatments (unusual responses to various drugs)
Analysis of performance statistics (professional athletes)
Weather Prediction
Financial Applications (loan approval, stock tracking)
“One persons noise could be another person’s signal.”

5. What is an outlier?
Observations inconsistent with rest of the dataset – Global Outlier
Special outliers – Local Outlier
Observations inconsistent with their neighborhoods
A local instability or discontinuity

6. Causes of Outliers Poor data quality / contamination
Low quality measurements, malfunctioning equipment, manual error
Correct but exceptional data

7. Outlier Detection Approaches
Objective:
Define what data can be considered as inconsistent in a given data set
Statistical-Based Outlier Detection
Deviation-Based Outlier Detection
Distance-Based Outlier Detection
Find an efficient method to mine the outliers

8. Why A Special Technique to Identify Outliers?
Why not just modify clustering or other algorithms to detect outliers?
Performance considerations
Subjective to the clustering algorithm and clustering parameters
Only certain attributes may have outlier properties, no need to disqualify the entire tuple
Contamination may occur by “column”, not by row

9. Outlier Analysis - Outline
Introduction / Motivation / Definition
Statistical-based Detection
Distribution-based, depth-based
Deviation-based Method
Sequential exception, OLAP data cube
Distance-based Detection
Index-based, nested-loop, cell-based, local-outliers
Questions

10. Statistical-Based Outlier Detection (Distribution-based)
Assumptions:
Knowledge of data (distribution, mean, variance)
Statistical discordancy test
Data is assumed to be part of a working hypothesis (working hypothesis)
Each data object in the dataset is compared to the working hypothesis and is either accepted in the working hypothesis or rejected as discordant into an alternative hypothesis (outliers)

11. Statistical-Based Outlier Detection (Distribution-based)
Assumptions:
Knowledge of data (distribution, mean, variance)
Statistical discordancy test
Data is assumed to be part of a working hypothesis (working hypothesis)
Each data object in the dataset is compared to the working hypothesis and is either accepted in the working hypothesis or rejected as discordant into an alternative hypothesis (outliers)

12. Statistical-Based Outlier detection (Depth-based)
Data is organized into layers according to some definition of depth
Shallow layers are more
likely to contain
outliers than deep
layers
Can efficiently handle
computation for k < 4

13. Statistical-Based Outlier Detection
Strengths
Most outlier research has been done in this area, many data distributions are known
Weakness
Almost all of the statistical models are univariate (only handle one attribute) and those that are multivariate only efficiently handle k<4
All models assume the distribution is known –this is not always the case
Outlier detection is completely subjective to the distribution used

14. Outlier Analysis - Outline
Introduction / Motivation / Definition
Statistical-based Detection
Distribution-based, depth-based
Deviation-based Method
Sequential exception, OLAP data cube
Distance-based Detection
Index-based, nested-loop, cell-based, local-outliers
Questions

15. Deviation-Based Outlier Detection
Simulate a mechanism familiar to human being: after seeing a series of similar data, an element disturbing the series is considered an exception
Sequential Exception Techniques
OLAP Data Cube Techniques

16. Sequential Exception
Select subsets of data Ij (j=1,2,…,n) from the dataset I
Compare the dissimilarity of I and (I-Ij)
Find out the minimum subset Ij that reduce the disimuliarity the most
Smoothing factor
D is a dissimilarity function
C is a cardinality function, for example, the number of elements in the dataset

17. Example Let the data set I be the set of integer values {1,4,4,4}Ij
I- Ij
C(I- Ij)
D(I- Ij)
SF(Ij) {}
{1,4,4,4} 4
1.69
0.00
{4}
{1,4,4} 3
2.00
-0.93
{4,4}
{1,4} 2
2.25
-1.12
{4,4,4}
{1} 1
5.07
3.38
Note, when Ij = {}, D(I) = D(I-Ij) = 1.69, SF(Ij)=0
When Ij={1}, SF(Ij) has the maximum value, so {1} is the outlier set

18. OLAP Data Cube Technique
Deviation detection process is overlapped with cube computation
Precomputed measures indicating data exceptions are needed
A cell value is considered an exception if it is significantly different from the expected value, based on a statistical model
Use visual cues such as background color to reflect the degree of exception

19. Outlier Analysis - Outline
Introduction / Motivation / Definition
Statistical-based Detection
Distribution-based, depth-based
Deviation-based Method
Sequential exception, OLAP data cube
Distance-based Detection
Index-based, nested-loop, cell-based, local-outliers
Questions

20. Distance-Based Outlier Detection
Distance-based: An object O in a dataset T is a DB(p,D) outier if at least fraction p of the objects in T are >= distance D from O
A point O in a dataset is an outlier with respect to parameters k and d if no more than k points in the dataset are at a distance of d or less from O.
Relative measurement: Let Dk(O) denote the distance of the kth nearest neighbor of O. It is a measure of how much of an outlier point O is.

21. Index-based Algorithm [KN98]
Indexing Structures such as R-tree (R+-tree), K-D (K-D-B) tree are built for the multi-dimensional database
The index is used to search for neighbors of each object O within radius D around that object.
Once K (K = N(1-p)) neighbors of object O are found, O is not an outlier.
Worst-case computation complexity is O(K*n2), K is the dimensionality and n is the number of objects in the dataset.
Pros: scale well with K
Cons: the index construction process may cost much time

22. Nested-loop Algorithm [KN98]
Divides the buffer space into two halves (first and second arrays)
Break data into blocks and then feed two blocks into the arrays.
Directly computes the distance between each pair of objects, inside the array or between arrays
Decide the outlier.
Here comes an example:…
Same computational complexity as the index-based algorithm
Pros: Avoid index structure construction
Try to minimize the I/Os

23. Example – stage 1 Buffer DB A is the target block on stage 1
Load A into the first array (1R)
Load B into the second array (1R)
Load C into the second array (1R)
Load D into the second array (1R)
Total: 4 Reads A B A B C D
Starting Point of Stage 1 A D A B C D
End Point of Stage 1

24. Example Example – stage 2 Buffer DB D is the target block on stage 2
D is already in the buffer (no R)
A is already in the buffer (no R)
Load B into the first array (1R)
Load C into the first array (1R)
Total: 2 Reads A D A B C D
Starting Point of Stage 2 C D A B C D
End Point of Stage 2

25. Example – stage 3 Buffer DB C is the target block on stage 3
C is already in the buffer (no R)
D is already in the buffer (no R)
Load A into the second array (1R)
Load B into the second array (1R)
Total: 2 Reads C D A B C D
Starting Point of Stage 3 C B A B C D
End Point of Stage 3

26. Example Example – stage 4 Buffer DB B is the target block on stage 4
B is already in the buffer (no R)
C is already in the buffer (no R)
Load A into the first array (1R)
Load D into the first array (1R)
Total: 2 Reads
Every block is ¼ of the DB. From stage 1-4, a grand total of 10 blocks are read, amounting to 10/4 passes over the entire dataset. C B A B C D
Starting Point of Stage 4 D B A B C D
End Point of Stage 4

27. Cell-Based Algorithm [KN98]
Divide the dataset into cells with length
K is the dimensionality, D is the distance
Define Layer-1 neighbors – all the intermediate neighbor cells. The maximum distance between a cell and its neighbor cells is D
Define Layer-2 neighbors – the cells within 3 cell of a certain cell. The minimum distance between a cell and the cells outside of Layer-2 neighbors is D
Criteria
Search a cell internally. If there are M objects inside, all the objects in this cell are not outlier
Search its layer-1 neighbors. If there are M objects inside a cell and its layer-1 neighbors, all the objects in this cell are not outlier
Search its layer-2 neighbors. If there are less than M objects inside a cell, its layer-1 neighbor cells, and its layer-2 neighbor cells, all the objects in this cell are outlier
Otherwise, the objects in this cell could be outlier, and then need to calculate the distance between the objects in this cell and the objects in the cells in the layer-2 neighbor cells to see whether the total points within D distance is more than M or not.
An example

28. Example Red – A certain cell Yellow – Layer-1 Neighbor Cells
Blue – Layer-2 Neighbor Cells
Notes:
The maximum distance between a point in the red cell and a point In its layer-1 neighbor cells is D
The minimum distance between
A point in the red cell and a point outside its layer-2 neighbor cells is D

29. Distance-Based Outlier Detection (Local Outliers)
Some outliers can be defined as global outliers, some can be defined as local outliers to a given cluster
O2 would not normally be considered an outlier with regular distance-based outlier detection, since it looks at the global picture

30. Distance-Based Outlier Detection (Local Outliers)
Each data object is assigned a local outlier factor (LOF)
Objects which are closer to dense clusters receive a higher LOF
LOF varies according to the parameter MinPts

31. Distance-Based Outlier Detection (Local Outliers)

32. Distance-Based Outlier Detection (Partition-based)
Partition-based detection
Use BIRCH clustering to identify clusters/partitions of non-outliers
Prune partitions that do not contain outliers
Use Index/Nested Loop algorithms on the remaining data points
Since many data point are removed during pruning, the efficiency is increased significantly.

33. Outlier Analysis - Outline
Introduction / Motivation / Definition
Statistical-based Detection
Distribution-based, depth-based
Deviation-based Method
Sequential exception, OLAP data cube
Distance-based Detection
Index-based, nested-loop, cell-based, local-outliers
Questions